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The storage size of the image and video repositories are growing day by day due to the extensive use of digital image acquisition devices. The position of an object within an image is obtained by analyzing the content-based properties like shape, texture, and color, while compositional properties present the image layout and include the photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that divide each image into nine square areas. According to this rule, salient objects of an image are placed on the intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique, which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis performed on five image collections show an encouraging robustness of the proposed technique that is compared with the recent CBIR techniques.

Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model